Insurers Should Leverage All Available Data to Provide Better Premium Pricing
Life insurance is an important tool for protecting individuals and their families from the
financial consequences of unexpected events such as illness, disability, or death. In
Bangladesh, life insurance premium pricing is primarily based on mortality rates, which
are derived from life tables that provide statistical data on life expectancy and death
rates based on age and gender. Using only mortality rates to determine premium pricing
can have several disadvantages, and there are opportunities to leverage additional data
and artificial intelligence (AI) to optimize pricing and improve outcomes both for
insurers and the customers.
One of the major disadvantages of mortality rates-based pricing is its limited accuracy.
Life tables provide an estimate of average life expectancy and mortality rates but cannot
specify individual outcomes. Factors such as health status, lifestyle habits including
smoking, occupation can greatly affect individual life expectancy, and life tables do not
accurately reflect these variables. Absence of such critical information in the rate
making exposes insurers to the adverse selection effect. Adverse selection occurs when
individuals who are at higher risk of death are more likely to purchase life insurance,
which can result in higher claims costs for insurers. This can ultimately lead to higher
premiums for all customers, including those who are at lower risk of death.
Life tables are typically based on historical data, and do not necessarily account for
changes in risk factors over time. This is a serious drawback. For example, as medical
advancements occur, mortality rates may decrease, but life tables may not reflect this
change. In addition to that these tables have limited applicability to non-life insurance
because they are primarily designed for life insurance products and may not be
applicable to other types of insurance such as health or disability insurance.
To address these challenges and optimize life insurance premium pricing, insurers can
leverage additional data and AI to develop more sophisticated risk models. By
incorporating data on above-mentioned individual risk factors along with family
medical history, individual claims history etc., insurers can develop more accurate risk
profiles and determine premiums that are more closely aligned with an individual's
unique risk level. This can result in more affordable premiums for low-risk individuals,
while also ensuring that high-risk individuals pay premiums that are commensurate
with their risk level.
The use of electronic health records (EHRs) is an example how insurers can leverage
additional data to optimize pricing. The EHRs contain valuable data on an individual's
health condition, medical history, and treatments, which can be used to develop more
accurate risk profiles. By analyzing these data through AI algorithms, insurers can
identify patterns and correlations between different risk factors and develop more
accurate risk models that can improve existing pricing strategies. Insurers should
partner with local hospitals to develop integrated databases and leverage these
resources.
Non-life insurers can use telematics data to improve their rate making models.
Telematics data is collected through sensors in vehicles and can provide valuable
insights into an individual's driving habits, such as speed, acceleration, and braking. By
analyzing this data insurers can develop more accurate risk profiles for auto insurance
and decide premiums that are better aligned with an individual's driving behavior.
Insurers have opportunities to use additional data sources, such as social media data
and credit scores, if available. By combining these data with mortality rates and other
traditional risk factors, insurers can develop more sophisticated risk models to predict
individual risk levels and determine personalized premiums matching unique risk
profiles.
Mortality rates provide valuable insights into the probability of death based on age and
gender. However, life insurance premium pricing relying only on mortality rates is not
optimized and prone to adverse selection. By leveraging additional data sources and
various techniques of AI, insurers can develop more advanced risk models that are
better able to predict individual risk levels and determine customized premiums. This
can result in more affordable rate for low-risk individuals, while also ensuring that high-
risk individuals pay dividends that are commensurate with their risk levels. The use of
additional data and AI has the potential to improve outcomes for insurers and their
customers as well as increasing customer loyalty and satisfaction.
Dr. Nurur Rahman is the Founder and CEO of Somikoron, an AI-based insurtech
startup in Bangladesh. Somikoron is paving the way for Bangladesh insurance, finance,
and retails industries to be compatible with the Fourth Industrial Revolution. Contact:
[email protected]; [email protected]
